Archivio Istituzionale della Ricerca- Università del Salento
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Reading profile in deaf adults
No full textWe examined the reading profile of deaf subjects, focusing on psy- cholinguistic variables and considering the impact of the global factor, to deter- mine whether phonological, lexical, and semantic effects would differ in some way for these individuals in comparison to hearing individuals in reading. Thir- teen deaf young adults proficient in both oral lipreading and sign language were compared to a group of hearing subjects matched for gender, age, and education. Deaf participants had longer vocal reaction times in reading aloud single words with respect to hearing subjects. However, they showed a similar reading profile to controls, being affected by psycholinguistic variables in a very similar way. Deaf individuals did not show a multiplicative effect as a function of word difficulty in their reading slowness but only a constant delay. Overall, the deficit shown by deaf participants was relatively limited and not associated with specific cognitive pro- cesses. This finding is in keeping with the idea that at least some individuals with a severe hearing impairment may reach reasonably high levels of word reading
Lenvatinib Is Highly Effective in Patients with Hepatocellular Carcinoma Related to Both Metabolic Dysfunction-Associated Steatohepatitis and Alcoholic Etiology: A Propensity Score Analysis
No full text: Background and aims: Metabolic dysfunction-associated steatotic liver disease (MASLD)-related hepatocellular carcinoma (HCC) may have distinct biological characteristics influencing systemic treatment response. However, the prognostic impact of MASLD vs. alcohol-related HCC in patients receiving lenvatinib remains unclear. This study aimed to assess lenvatinib's effectiveness and safety in these populations. Methods: A multicenter cohort of 378 HCC patients treated with lenvatinib (2019-2024) was analyzed. Propensity score matching was performed based on age, sex, tumoral stage, alpha-fetoprotein levels and Child-Pugh class. Survival was estimated using Kaplan-Meier analysis and compared with the log-rank test. Results were expressed as HR and 95% CI. Results: After matching, 115 patients per group were compared. Median OS was 21 months (95% CI: 20-23) in the group with metabolic dysfunction-associated steatohepatitis (MASH) and 19 months (95% CI: 18-21) in the group with alcohol etiology (p = 0.18). In multivariate analysis, only Child-Pugh class (HR 2.67, 95% CI: 1.84-5.41) and tumor stage (HR 2.18, 95% CI: 1.57-6.93) resulted as significant predictors of OS. Median PFS was 9 months (95% CI: 8-9) in patients with MASH and 9 months (95% CI: 7-10) in patients with alcohol etiology (p = 0.33). Only the Child-Pugh class was a significant predictor of PFS in univariate analysis (HR 1.56, 95% CI: 1.15-3.41; p = 0.03). No difference in terms of adverse event rate was observed between the two groups. Conclusions: Lenvatinib is effective in patients with both MASH- and alcohol-related HCC, with no difference in oncological outcomes between the two groups
Accesso ancora negato. Discipline STEM, processi di esclusione e inclusione delle donne e strategie per l’empowerment femminile
Get PDFRadiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia
Get PDFNetwork modeling is increasingly used to study brain alterations in neurological disorders. In this study, we apply a novel modeling approach based on the similarity of regional radiomics feature to characterize gray matter network changes in patients with behavioral variant frontotemporal dementia (bvFTD) using MRI data. In this cross-sectional study, we assessed structural 3 T MRI data from twenty patients with bvFTD and 20 cognitively normal controls. Radiomics features were extracted from T1-weighted MRI based on cortical and subcortical brain segmentation. Similarity in radiomics features between brain regions was used to construct intra-individual structural gray matter networks. Regional mean connectivity strength (RMCS) and region-toregion radiomics similarity were compared between bvFTD patients and controls. Finally, associations between network measures, clinical data, and biological features were explored in bvFTD patients. Relative to controls, patients with bvFTD showed higher RMCS values in the superior frontal gyrus, right inferior temporal gyrus and right inferior parietal gyrus (FDR-corrected p |0.7|, p < 0.005). Our study provides new insights into frontotemporal network changes associated with bvFTD, highlighting specific associations between network measures and clinical/biological features. Radiomics feature similarity analysis could represent a useful approach for characterizing brain changes in patients with frontotemporal dementia
Shadow AI: Cyber Security Implications, Opportunities and Challenges in the Unseen Frontier
Get PDFThe progression of artificial intelligence (AI) technologies has reached a level that greatly enhances the different organizational sectors by facilitating them with the means to advance and improve systems and processes. Shadow AI implies the usage of AI tools and systems by individuals within an entity, respectively, without permission thereby implying that these tools were not directly monitored or controlled by the centralized IT or security department. It also contributes to significant cyber risks such as data and security breaches, abuse of compliance, and, in general, an increased threat landscape. This paper highlights into the emerging global security trends and Shadow AI while also covering the unique positioning within the threat landscape concerning unauthorized computation of sensitive data, safety vulnerabilities of the unmonitored AI models, and model poisoning alongside data leakage-marked out. Moreover, this paper covers how Shadow AI distracts the attack landscape while increasing the level of security problem for the organization. Shadow AI, however, can be employed to increase the ability to respond to threats, locate irregularities, and increase the range of options available for cyber solutions even with all its risks
Intermediates Trade and Knowledge Flows
No full textThis paper introduces a novel mechanism by emphasizing benefits for firms through participation in buyer networks among firms that source the same locally produced inputs. In a first step, we utilize register-based data from Denmark to generate a firmspecific buyer network variable which relies on firms’ industrial input structures and imports. Utilizing this proxy we provide evidence of cost savings from network participation, as larger buyer networks reduce firms’ input demand. Subsequently, we develop a trade model incorporating vertical linkages and introduce network effects that result in savings in intermediate costs. Our theory posits that the magnitude of these savings may be associated with the effectiveness of knowledge transmission among network participants. Consequently, firms operating in regions with efficient knowledge transmission networks may realize greater savings in intermediate input costs, leading to increased profits from local and export sales. In a last step, we provide empirical evidence supporting our theoretical predictions by demonstrating the positive impact of buyer networks based on relationship-specific products on domestic firm revenues
Exploring the Role of Oleic Acid in Muscle Cell Differentiation: Mechanisms and Implications for Myogenesis and Metabolic Regulation in C2C12 Myoblasts
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Hyper-Parameters Effects in Conditional Diffusion Models for Accurate Sea Surface Temperature Reconstruction
Get PDFAccurate representation of oceanic conditions is fundamental for reliable climate modeling, weather forecasting, and environmental monitoring. However, ocean models and observational datasets often exhibit systematic biases due to limitations in model physics, parameterizations, resolution, or ob servational coverage. In this work, we propose a diffusion model for bias correction. We systematically evaluated its performance for Sea Surface Temperature on the oceanic sea surface temperature generation by varying different hyperparameters in the U-Net architecture. The model is trained to denoise simulated data and reconstruct the SST field guided by reanalysis data. Our results demonstrate that increasing the base channel's depth significantly improves the model's performance, with improvements in convergence speed, reconstruction accuracy, and spatial detail retention. Quantitative metrics such as root mean squared error (RMSE), Pearson's correlation coefficient (PCC), and coefficient of determination (R2) show notable gains up to a base channel depth of 64, beyond which performance gains plateau. A detailed temporal generalization analysis using seasonal batches every two months confirms the robustness of the model in varying SST regimes. At the same time, qualitative visualizations show sharp and coherent reconstructions with minimal error. The study highlights the trade-off between model complexity and performance and identifies 64 base channels as a computationally efficient and accurate configuration for SST modeling using diffusion-based generative methods